import simulacao.sumario as sm
import simulacao.random as rd
import plotly.graph_objects as go
sm = sm.Sumario(n_samples=10)
header = ['Amostras', 'size', 'mean', 'mode', 'median', 'variance', 'std', 'kurtosis', 'skewness', 'Q1', 'Q3']
formating = [None, 'd','.2f','.2f','.2f','.2f','.2f','.2f','.2f','.2f','.2f']
rand = rd.Random()
x = rand.binomial(n=10, p=0.5, size=100000)
fig = go.Figure(data=[go.Histogram(x=x, histnorm='probability')])
fig.show()
## for size = 1000
esp, obs = sm.binomial(n=10, p=0.5, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.binomial(n=10, p=0.5, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.binomial(n=10, p=0.5, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
rand = rd.Random()
x = rand.geometric(p=0.5, size=100000)
fig = go.Figure(data=[go.Histogram(x=x, histnorm='probability')])
fig.show()
esp, obs = sm.geometric(p=0.5, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.geometric(p=0.5, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.geometric(p=0.5, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
rand = rd.Random()
x = rand.triangular(lower=0, mode=0.5, upper=1, size=100000)
fig = go.Figure(data=[go.Histogram(x=x, histnorm='probability')])
fig.show()
esp, obs = sm.triangular(lower=0., upper=1., mode=0.5, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.triangular(lower=0., upper=1., mode=0.5, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.triangular(lower=0., upper=1., mode=0.5, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
rand = rd.Random()
x = rand.weibull(alpha=1., beta=1., size=100000)
fig = go.Figure(data=[go.Histogram(x=x, histnorm='probability')])
fig.show()
esp, obs = sm.weibull(alpha=1, beta=1, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.weibull(alpha=1, beta=1, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()
esp, obs = sm.weibull(alpha=1, beta=1, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))
fig = go.Figure(data=[go.Table(header=dict(values=header),
cells=dict(values=table_t, format = formating))])
fig.show()